big risks requires big data thinking

Forensic Data Analytics

2015

Big risks requires big data thinkingForensic data analytics use cases

Vincent WaldenPartner, EYNovember 17, 2015

Agenda

► Key analytics trends in fraud risk management► “Big data thinking”► Anti-fraud use case examples:

► Employee and vendor transaction risk scoring► Payment stream analysis► Text mining and dashboards to find potentially improper payments► Social media analytics► Email analytics, emotive tone and Fraud Triangle Analytics► Cyber monitoring and events

► Integrating visualization into your risk managementplatform

The forensic data analytics landscape

► The regulators are upping their game► Be ready - the regulators are investing in advanced monitoring

technology

► Big risks requires “big data” thinking► New approaches to counter fraud and compliance monitoring,

beyond simple rules-based tests

► Compliance fatigue? Analytics can help► Analytics can help improve efficiency and program effectiveness to

help compliance functions audit and monitor smarter – saving bothtime and valuable resources

Upping their game: SEC priorities aroundforensic data analytics

-U.S. SEC Chair Mary Jo White, prepared testimonybefore the Senate Appropriations Subcommittee,May 14, 2014

FDA business landscapeData analytics is continued focus area in guidance

COSO: Internal Controls IntegratedFramework1. Principal #8: Fraud Risk Assessment (COSO 2013)2. New guidance coming in December 2015 will have

significant focus on the use of proactive forensicsdata analytics

ACFE Report to the Nation on Occupational Fraud1. For those companies with proactive data analytics in place, the

cost per fraud incident was 59.7% lower (roughly $100,000lower per incident) than those companies not using proactivedata analytics – more than any other control listed in thesurvey.

2. Further, the median duration of fraud based on the presence ofproactive data analytics was half the time at 12 months vs 24months.

See 2014 ACFE Report the Nations on Occupational Fraud, Figures 37 and 38

Forensic data analytics maturity modelBeyond traditional “rules-based queries” – consider all four quadrants

False Positive RateHigh Low

Stru

ctur

edD

ata

Detection RateLow High

Uns

truc

ture

dD

ata

“Traditional” rules-Based Queries &Analytics

Matching, Grouping, Ordering,Joining, Filtering

Statistical-Based Analysis

Anomaly Detection, ClusteringRisk Ranking

Traditional Keyword Searching

Keyword Search

Data Visualization & Text Mining

Data visualization, drill-down intodata, text mining

Big data thinking

Definition of Big Data

Gartner: Big Data is high volume,velocity and variety information assetsthat demand cost-effective, innovativeforms of information processing forenhanced insight anddecision making.

Big data techniques for counter fraud

► Multiple data sources

► Data visualization

► Text analytics

► Payment/transaction risk scoring

► Predictive modeling – technology assisted monitoring

► Pattern & link analysis

► Flexible deployment models

Anti-fraud use case examples

Employee risk scoring - Travel &entertainment expense monitoring

Vendor risk scoring - potentially improperpayments

Text mining dashboard - paymentdescriptions

Text mining dashboard – drill down

Text mining dashboard – word clouds andstratification

Social media analytics

Email analytics: Emotive tone – secretive,angry, derogatory emails

Email analytics: Fraud triangle analytics

Fraud Triangle Analytics: Pressure/Opportunity/RationalizationEmployee term analysis

Term hit frequency over time

Cyber monitoring

Surveillance monitoring: executive dashboard

► Aggregate view of risks,by incident

► Quick synopsis ofrisk profile

Surveillance monitoring: management dashboardRisk ranking summary at the trader (employee) level

► Risk score by personnel ► Interactive dashboards

Management alert screenTrader alert initiation

► Create customizedalerts

► Transparency across multiple data sources:trades, voice, email, chat, entertainment, etc.

Trader communication review screen – textanalytics using Watson Content Analytics

► Sentiment analysishighlighted using WCA

► Issue codingand tagging

Integrating visualization into your riskmanagement platform

How is fraud detected?50% by tip or accident demonstrates the need

for improved analytics

2014 ACFE Report to the Nation on Occupational Fraud

Start with the “Fraud Tree” of schemes

Fraud tree

Cashlarceny

Theft ofother assets– inventory/

AR/fixed assets

Revenuerecognition

Nonfinancial

Conflictsof

interest

Bribery andcorruption/

FCPAIllegal

gratuitiesBid-rigging/procurement

Corruption Fraudulent statements

Asset misappropriation

Fakevendor

Payrollfraud

T&Efraud

Theft ofdata

GAAP Reserves

General focus of auditors

General focus ofinternal auditors

General focus of the regulators(opportunity for Auditors and Investigators)

Today’s biggest forensic data analytics (FDA)challenges

Source: 2014 EY Global Forensic Data Analytics Survey (www.ey.com/fdasurvey)

2%

3%

3%

4%

5%

5%

6%

6%

8%

9%

10%

10%

15%

15%

26%

0% 5% 10% 15% 20% 25% 30%

Uncertainty about the relevance of FDA in the Company

FDA producing positive results to indicate and prove any fraud or…

FDA is not prevalent to the culture

Huge volume of data to analyze

To identify fraudulent information across large data sets

Lack of human resources or manpower to operate FDA

Spreading the FDA culture across different Business Units

Difficulty in adapting FDA to comply with different regulations in…

Poor quality or lack of accuracy in the data

To prevent fraud rather than discover fraud

FDA is too expensive

Convincing senior management or the company about the benefits of…

Improving the quality of the analysis process

Challenges with combining data across various IT systems

Getting the right tools or expertise for FDA

Integrating dashboards into an boarder fraud riskmanagement platform

Visualization: Detectfraud within a businessprocess

Case Management: Assigntasks, flag transactions anddelegate projects for review

Statistical: Apply fraudinsights and automatedalerts to take action inreal or near time –when it matters

Pattern & Link: Uncoverhidden fraud andrelationships

Detect

Investigate

Respond

Discover

An enterprise approach, based on solutions

Entity and SocialNetwork analytics

Predictiveanalytics

Behavioral /Geospatial

PrioritizedIncidents

Businessintelligence

Context / Textanalytics

Decisionmanagement

Contentmanagement

Casemanagement

Forensicanalysis

Beneficiaries

Legal & compliance(including M&A)

Internal Audit

Big Data, scalable platform, delivered on desktop or mobile device

► Flexible approaches, reports andcapabilities for each beneficiary

► Changing risks requires flexible tools► Knowing “who is who” is key to

identifying patterns & opportunities► Reduced false positives, better ROI► Cross enterprise view of exposures► Expedient audits/ investigations► Data transparency, no “black box”

Data Governance and Collaboration

Shared Services& Finance

BU Leadership& Corporate

Internal Sources

External Sources

Otherbeneficiaries

Enterprise PlatformSecurity

intelligence &Cyber

Socialmedia feeds

Shared svcs.data feeds

ERP systems

Sanctions &watchlists

News feeds &adverse media

Internalreports &communications

Master &reference data

EmbeddedIntelligence

ActivityMonitoring

Dark Web

Five success factors in deploying FDA

1. Focus on the low hanging fruit, the priority of the first projectmatters

2. Go beyond traditional “rules-based” tests – incorporate big datathinking

3. Communicate: share information on early successes acrossdepartments / business units to gain broad support

4. Leadership gets it funded, but interpretation of the results byexperienced or trained professionals make the program successful

5. Enterprise-wide deployment takes time, don’t expect overnightadoption

Questions or discussion

Contact information

Vincent WaldenPartner, [email protected]

big risks requires big data thinking

Data & Analytics